Various N-of-M channel selection strategies for cochlear implant systems have been described in which a cochlear implant system only stimulates a subset of the available electrodes in each given stimulation frame. In these N-of-M channel selection strategies, a cochlear implant system divides an incoming audio signal into M analysis channels (or simply “channels”) and then presents only N analysis channels to the patient (e.g., by applying electrical stimulation representative of the signals contained within the N analysis channels by way of a plurality of intracochlear electrodes).
In one conventional N-of-M channel selection strategy, a cochlear implant system selects only the analysis channels with the highest amplitude signals for presentation to a patient during a particular stimulation frame. This means that all information in the lower amplitude channels is lost during that frame. This could be disadvantageous in situations where the overall frequency distribution remains relatively constant for a period of time, such as when the patient is listening in certain noisy environments or detecting background sounds during vowels. One example of this would be someone honking a horn while someone is talking. If the horn is loud enough, its spectral content would overwhelm the talker, and this N-of-M channel selection strategy would only deliver envelope information to the pulse generator for those channels which contain “horn content”. All of the other channels would be effectively muted.
Other N-of-M channel selection strategies use temporal masking characteristics to select the most relevant channels for presentation to a patient during a particular stimulation frame. These temporal masking approaches take into account the refractory phenomena associated with auditory nerve fibers by decreasing the probability that a particular channel that has been selected for presentation during a stimulation frame will be again selected for presentation during one or more stimulation frames that immediately follow the stimulation frame. However, these temporal masking approaches have heretofore been computationally intensive, requiring computation of masking models during each stimulation frame. Moreover, these temporal masking approaches do not specifically increase the probability that a particular channel that has not been selected for a relatively long time will be again selected, thus causing the patient to miss out on the information contained within that channel.